Moving objects have changing and repeating patterns due to their movements in space over time. A set of observations on a moving object can be used to derive the movement patterns by summarizing frequently visited locations and frequently used paths over time by the object. These space- and time-dependent movement patterns of an object are referred to as spatio-temporal movement signatures in this paper. There is a need to understand and identify the spatio-temporal movement signatures. Analysis of the spatio-temporal movement signatures can help improve the modelling, querying, and reasoning capabilities of the movements of objects. This paper demonstrates the extraction of movement signatures from sets of movement observations using Visualization and Resilient Neural Network methodologies. Identification of movement signatures and definition of their attributes provide summary-level information for modelling and reasoning about moving objects.